A brain-based device (BBD) is a device that has a sensing system for receiving information, effectors that enable the device to move about, and a simulated nervous system which controls movement of the effectors in response to input from the sensing system to guide the behavior of the brain-based device in a real-world environment. The sensing system may include sensors which receive image and other information from the real-world environment in which the device moves. The simulated nervous system may be implemented as a computer-based system which receives and processes the sensed information input to the brain-based device and outputs commands to the effectors to control the behavior of the device (BBD) in the real-world environment.
The simulated nervous system, while implemented in a computer-based system, emulates the human brain rather than a programmed computer which typically follows a set of precise executable instructions or which performs computations. That is, the brain is not a computer and follows neurobiological rather than computational principles in its construction. The brain has special features or organization and functions that are not believed to be consistent with the idea that it follows such a set of precise instructions or that it computes in the manner of a programmed computer. A comparison of the signals that a brain receives with those of a computer shows a number of features that are special to the brain. For example, the real world is not presented to the brain like a data storage medium storing an unambiguous series of signals that are presented to a programmed computer. Nonetheless, the brain enables humans (and animals) to sense their environment and move in a real-world environment.
The brain's cerebellum is known to be critical for accurate adaptive control and motor learning. One theory of the cerebellum, consistent with much of the neurophysiological, behavioral, and imaging data regarding motor control, proposes that the cerebellum learns to replace reflexes with a predictive controller. This produces a correct motor control signal and circumvents less adaptive reflexive responses. Numerous adaptive cerebellar functions, including eye-blink conditioning, the vestibular-ocular reflex, smooth pursuit eye movement, spinal nociceptive withdrawal reflex, grip force adjustments, arm movements, and saccadic eye movements, are susceptible to this type of motor control. At present, debate about the mechanisms responsible for this predictive capability include proposals for delay lines, spectral timing, oscillators, or dynamic recurrent activity in granule cells.
One current theory proposes that a feedback motor command from a primitive feedback controller (or reflex) is used as an error signal delivered to the cerebellum via climbing fibers from the inferior olive of the brain. In addition, synaptic eligibility traces in the cerebellum has also been proposed as a mechanism for such motor learning. Yet another theory proposes an eligibility trace that is triggered by motion onset and peaks at 150-200 ms with durations of 1-2 seconds.